A robust Machine Learning (ML)-based framework for accurately locating electrical faults in wind farm collector networks and achieves the highest accuracy, with prediction errors not exceeding 2%.
Abstract
The increasing integration of Inverter-Based Resources (IBRs) into modern power grids has introduced new challenges for conventional fault-location techniques, particularly for renewable sources such as wind and solar. To address this issue, this paper proposes a robust Machine Learning (ML)-based framework for accurately locating electrical faults in wind farm collector networks. The proposed methodology comprises multiple stages. Initially, 14 different regression models were implemented and evaluated using the Scikit-Learn library to assess their suitability for the task. Subsequently, a comprehensive hyperparameter optimization phase was conducted using Optuna, aiming not only to enhance model accuracy but also to reassess previously under-performing algorithms. The resulting models were then validated under a wide range of operating conditions, including variations in fault resistance, fault location, fault inception angle, and wind farm generation level. By exposing these models to 18,600 distinct fault scenarios, the simulations provide a comprehensive assessment of their generalization capability and demonstrate the effectiveness of the proposed approach. Among all models tested, the Multi-Layer Perceptron Regressor (MLP), Support Vector Regressor (SVR), and Kernel Ridge Regression (KRR) achieved the highest accuracy, with prediction errors not exceeding 2%. Conversely, ensemble-based methods such as Random Forest, AdaBoost, and Gradient Boosting exhibited noticeable limitations, struggling to capture the complex nonlinear relationships between fault characteristics and their corresponding locations in the context of IBRs.
The growing penetration of Inverter-Based Resources (IBRs), such as solar and wind, challenges the operation of electric power systems. Specifically, conventional methods for fault location (FL) in wind farm collector lines experience reduced accuracy due to the atypical contributions of IBRs and the inherent non-homogeneity of these lines. This paper proposes a novel FL approach for wind farm collector systems that compensates for the errors introduced by these factors using measurements only at the circuit entrance. Additionally, the approach incorporates a mitigation strategy for multiple fault location estimation. To enhance practical applicability, the methodology includes a virtual meter model to estimate electrical quantities at remote nodes, reducing reliance on an expensive advanced metering infrastructure. The proposed method was validated through PSCAD/EMTDC simulations based on a real wind farm located in northeastern Brazil. The results demonstrate significant improvements over classical approaches, achieving an average reduction of up to 96% in FL errors and mitigating approximately 99.7% of cases with multiple location estimates.
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